Data as Commodity: a Game-Theoretic Principle for Information Pricing

Data is the central commodity of the digital economy. Unlike physical goods, it is non-rival, replicable at near-zero cost, and traded under heterogeneous licensing rules. These properties defy standard supply--demand theory and call for new pricing principles. We propose a game-theoretic approach in which the value of a data string emerges from strategic competition among N players betting on an underlying stochastic process, each holding partial information about past outcomes. A better-informed player faces a choice: exploit their informational advantage, or sell part of their dataset to less-informed competitors. By analytically computing the Nash equilibrium of the game, we determine the price range where the trade is beneficial to both buyer and seller. We uncover a rich landscape of market effects that diverge from textbook economics: first, prospective sellers and buyers can compete or jointly exploit the less informed competitors depending on the quality of data they hold. In a symbiotic regime, the seller can even share data for free while still improving her payoffs, showing that losing exclusivity does not necessarily reduce profit. Moreover, rivalry between well-informed players can paradoxically benefit uninformed ones, demonstrating that information abundance does not always translate to higher payoffs. We also show that the number of players influences the competition between informed parties: trades impossible in small markets become feasible in larger ones. These findings establish a theoretical foundation for the pricing of intangible goods in dynamically interacting digital markets, which are in need of robust valuation principles.